Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations45000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 MiB
Average record size in memory131.0 B

Variable types

Numeric8
Categorical2

Alerts

cb_person_cred_hist_length is highly overall correlated with person_age and 1 other fieldsHigh correlation
loan_amnt is highly overall correlated with loan_percent_incomeHigh correlation
loan_percent_income is highly overall correlated with loan_amntHigh correlation
person_age is highly overall correlated with cb_person_cred_hist_length and 1 other fieldsHigh correlation
person_emp_exp is highly overall correlated with cb_person_cred_hist_length and 1 other fieldsHigh correlation
person_income is highly skewed (γ1 = 34.13758313) Skewed
person_emp_exp has 9566 (21.3%) zeros Zeros

Reproduction

Analysis started2024-12-11 15:48:50.875494
Analysis finished2024-12-11 15:49:14.953576
Duration24.08 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

person_age
Real number (ℝ)

High correlation 

Distinct60
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.764178
Minimum20
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2024-12-11T21:19:15.260537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile22
Q124
median26
Q330
95-th percentile39
Maximum144
Range124
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.0451082
Coefficient of variation (CV)0.2177305
Kurtosis18.649449
Mean27.764178
Median Absolute Deviation (MAD)3
Skewness2.548154
Sum1249388
Variance36.543333
MonotonicityNot monotonic
2024-12-11T21:19:15.699971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 5254
11.7%
24 5138
11.4%
25 4507
10.0%
22 4236
9.4%
26 3659
 
8.1%
27 3095
 
6.9%
28 2728
 
6.1%
29 2455
 
5.5%
30 2021
 
4.5%
31 1645
 
3.7%
Other values (50) 10262
22.8%
ValueCountFrequency (%)
20 17
 
< 0.1%
21 1289
 
2.9%
22 4236
9.4%
23 5254
11.7%
24 5138
11.4%
25 4507
10.0%
26 3659
8.1%
27 3095
6.9%
28 2728
6.1%
29 2455
5.5%
ValueCountFrequency (%)
144 3
< 0.1%
123 2
< 0.1%
116 1
 
< 0.1%
109 1
 
< 0.1%
94 1
 
< 0.1%
84 1
 
< 0.1%
80 1
 
< 0.1%
78 1
 
< 0.1%
76 1
 
< 0.1%
73 3
< 0.1%

person_income
Real number (ℝ)

Skewed 

Distinct33989
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80319.053
Minimum8000
Maximum7200766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2024-12-11T21:19:16.135995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum8000
5-th percentile28366.7
Q147204
median67048
Q395789.25
95-th percentile166754.7
Maximum7200766
Range7192766
Interquartile range (IQR)48585.25

Descriptive statistics

Standard deviation80422.499
Coefficient of variation (CV)1.0012879
Kurtosis2398.6848
Mean80319.053
Median Absolute Deviation (MAD)23124
Skewness34.137583
Sum3.6143574 × 109
Variance6.4677783 × 109
MonotonicityNot monotonic
2024-12-11T21:19:16.553008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8000 15
 
< 0.1%
73011 10
 
< 0.1%
36995 9
 
< 0.1%
60914 8
 
< 0.1%
37020 8
 
< 0.1%
73082 7
 
< 0.1%
60864 7
 
< 0.1%
67131 7
 
< 0.1%
72951 7
 
< 0.1%
73040 7
 
< 0.1%
Other values (33979) 44915
99.8%
ValueCountFrequency (%)
8000 15
< 0.1%
8037 1
 
< 0.1%
8104 1
 
< 0.1%
8186 1
 
< 0.1%
8248 1
 
< 0.1%
8267 1
 
< 0.1%
8277 1
 
< 0.1%
8302 1
 
< 0.1%
8518 1
 
< 0.1%
9364 1
 
< 0.1%
ValueCountFrequency (%)
7200766 1
< 0.1%
5556399 1
< 0.1%
5545545 1
< 0.1%
2448661 1
< 0.1%
2280980 1
< 0.1%
2139143 1
< 0.1%
2012954 1
< 0.1%
1741243 1
< 0.1%
1728974 1
< 0.1%
1661567 1
< 0.1%

person_emp_exp
Real number (ℝ)

High correlation  Zeros 

Distinct63
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4103333
Minimum0
Maximum125
Zeros9566
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2024-12-11T21:19:16.988085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q38
95-th percentile17
Maximum125
Range125
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.0635321
Coefficient of variation (CV)1.1207317
Kurtosis19.168324
Mean5.4103333
Median Absolute Deviation (MAD)3
Skewness2.5949174
Sum243465
Variance36.766421
MonotonicityNot monotonic
2024-12-11T21:19:17.441513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9566
21.3%
2 4134
9.2%
1 4061
9.0%
3 3890
8.6%
4 3524
 
7.8%
5 3000
 
6.7%
6 2717
 
6.0%
7 2204
 
4.9%
8 1890
 
4.2%
9 1575
 
3.5%
Other values (53) 8439
18.8%
ValueCountFrequency (%)
0 9566
21.3%
1 4061
9.0%
2 4134
9.2%
3 3890
8.6%
4 3524
 
7.8%
5 3000
 
6.7%
6 2717
 
6.0%
7 2204
 
4.9%
8 1890
 
4.2%
9 1575
 
3.5%
ValueCountFrequency (%)
125 1
< 0.1%
124 1
< 0.1%
121 1
< 0.1%
101 1
< 0.1%
100 1
< 0.1%
93 1
< 0.1%
85 1
< 0.1%
76 1
< 0.1%
62 1
< 0.1%
61 1
< 0.1%

loan_amnt
Real number (ℝ)

High correlation 

Distinct4483
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9583.1576
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2024-12-11T21:19:17.869606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2000
Q15000
median8000
Q312237.25
95-th percentile24000
Maximum35000
Range34500
Interquartile range (IQR)7237.25

Descriptive statistics

Standard deviation6314.8867
Coefficient of variation (CV)0.65895678
Kurtosis1.3512152
Mean9583.1576
Median Absolute Deviation (MAD)3800
Skewness1.1797313
Sum4.3124209 × 108
Variance39877794
MonotonicityNot monotonic
2024-12-11T21:19:18.288573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 3617
 
8.0%
5000 2787
 
6.2%
6000 2426
 
5.4%
12000 2416
 
5.4%
15000 2004
 
4.5%
8000 1928
 
4.3%
4000 1406
 
3.1%
20000 1385
 
3.1%
3000 1378
 
3.1%
7000 1314
 
2.9%
Other values (4473) 24339
54.1%
ValueCountFrequency (%)
500 5
< 0.1%
563 1
 
< 0.1%
700 1
 
< 0.1%
725 1
 
< 0.1%
750 1
 
< 0.1%
800 1
 
< 0.1%
900 2
 
< 0.1%
912 1
 
< 0.1%
922 1
 
< 0.1%
950 1
 
< 0.1%
ValueCountFrequency (%)
35000 234
0.5%
34826 1
 
< 0.1%
34800 1
 
< 0.1%
34664 1
 
< 0.1%
34375 1
 
< 0.1%
34322 1
 
< 0.1%
34121 1
 
< 0.1%
34000 4
 
< 0.1%
33950 2
 
< 0.1%
33800 1
 
< 0.1%

loan_int_rate
Real number (ℝ)

Distinct1302
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.006606
Minimum5.42
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2024-12-11T21:19:18.724691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5.42
5-th percentile6.17
Q18.59
median11.01
Q312.99
95-th percentile16
Maximum20
Range14.58
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation2.9788083
Coefficient of variation (CV)0.27063823
Kurtosis-0.42033531
Mean11.006606
Median Absolute Deviation (MAD)2.13
Skewness0.21378407
Sum495297.26
Variance8.8732988
MonotonicityNot monotonic
2024-12-11T21:19:19.152107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.01 3329
 
7.4%
10.99 804
 
1.8%
7.51 798
 
1.8%
7.49 687
 
1.5%
7.88 673
 
1.5%
5.42 608
 
1.4%
7.9 606
 
1.3%
11.49 514
 
1.1%
9.99 484
 
1.1%
13.49 475
 
1.1%
Other values (1292) 36022
80.0%
ValueCountFrequency (%)
5.42 608
1.4%
5.43 2
 
< 0.1%
5.44 2
 
< 0.1%
5.46 1
 
< 0.1%
5.47 5
 
< 0.1%
5.48 4
 
< 0.1%
5.49 4
 
< 0.1%
5.5 1
 
< 0.1%
5.51 3
 
< 0.1%
5.52 2
 
< 0.1%
ValueCountFrequency (%)
20 84
0.2%
19.91 9
 
< 0.1%
19.9 1
 
< 0.1%
19.82 5
 
< 0.1%
19.8 1
 
< 0.1%
19.79 4
 
< 0.1%
19.74 4
 
< 0.1%
19.69 12
 
< 0.1%
19.66 3
 
< 0.1%
19.62 1
 
< 0.1%

loan_percent_income
Real number (ℝ)

High correlation 

Distinct64
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13972489
Minimum0
Maximum0.66
Zeros27
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2024-12-11T21:19:19.582402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03
Q10.07
median0.12
Q30.19
95-th percentile0.31
Maximum0.66
Range0.66
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.087212308
Coefficient of variation (CV)0.6241716
Kurtosis1.0824162
Mean0.13972489
Median Absolute Deviation (MAD)0.05
Skewness1.0345122
Sum6287.62
Variance0.0076059867
MonotonicityNot monotonic
2024-12-11T21:19:19.989888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08 2593
 
5.8%
0.1 2421
 
5.4%
0.07 2415
 
5.4%
0.09 2295
 
5.1%
0.06 2242
 
5.0%
0.12 2216
 
4.9%
0.05 2176
 
4.8%
0.11 2158
 
4.8%
0.14 1960
 
4.4%
0.04 1950
 
4.3%
Other values (54) 22574
50.2%
ValueCountFrequency (%)
0 27
 
0.1%
0.01 315
 
0.7%
0.02 944
 
2.1%
0.03 1488
3.3%
0.04 1950
4.3%
0.05 2176
4.8%
0.06 2242
5.0%
0.07 2415
5.4%
0.08 2593
5.8%
0.09 2295
5.1%
ValueCountFrequency (%)
0.66 1
 
< 0.1%
0.63 1
 
< 0.1%
0.62 2
 
< 0.1%
0.61 2
 
< 0.1%
0.59 1
 
< 0.1%
0.58 1
 
< 0.1%
0.57 1
 
< 0.1%
0.56 5
< 0.1%
0.55 5
< 0.1%
0.54 8
< 0.1%

credit_score
Real number (ℝ)

Distinct340
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean632.60876
Minimum390
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2024-12-11T21:19:20.419096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum390
5-th percentile539
Q1601
median640
Q3670
95-th percentile703
Maximum850
Range460
Interquartile range (IQR)69

Descriptive statistics

Standard deviation50.435865
Coefficient of variation (CV)0.079726789
Kurtosis0.20302186
Mean632.60876
Median Absolute Deviation (MAD)33
Skewness-0.61026083
Sum28467394
Variance2543.7765
MonotonicityNot monotonic
2024-12-11T21:19:20.835557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
658 406
 
0.9%
649 398
 
0.9%
652 396
 
0.9%
663 394
 
0.9%
647 393
 
0.9%
650 391
 
0.9%
654 391
 
0.9%
667 390
 
0.9%
653 390
 
0.9%
656 386
 
0.9%
Other values (330) 41065
91.3%
ValueCountFrequency (%)
390 1
 
< 0.1%
418 1
 
< 0.1%
419 1
 
< 0.1%
420 1
 
< 0.1%
421 1
 
< 0.1%
430 1
 
< 0.1%
431 2
< 0.1%
434 1
 
< 0.1%
435 4
< 0.1%
437 2
< 0.1%
ValueCountFrequency (%)
850 1
< 0.1%
807 1
< 0.1%
805 1
< 0.1%
792 1
< 0.1%
789 1
< 0.1%
784 2
< 0.1%
773 1
< 0.1%
772 1
< 0.1%
770 1
< 0.1%
768 1
< 0.1%

cb_person_cred_hist_length
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8674889
Minimum2
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2024-12-11T21:19:21.189198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median4
Q38
95-th percentile14
Maximum30
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.8797018
Coefficient of variation (CV)0.66122014
Kurtosis3.7259445
Mean5.8674889
Median Absolute Deviation (MAD)2
Skewness1.63172
Sum264037
Variance15.052086
MonotonicityNot monotonic
2024-12-11T21:19:21.535004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4 8653
19.2%
3 8312
18.5%
2 6537
14.5%
5 3082
 
6.8%
6 2966
 
6.6%
7 2889
 
6.4%
8 2800
 
6.2%
9 2685
 
6.0%
10 2457
 
5.5%
12 715
 
1.6%
Other values (19) 3904
8.7%
ValueCountFrequency (%)
2 6537
14.5%
3 8312
18.5%
4 8653
19.2%
5 3082
 
6.8%
6 2966
 
6.6%
7 2889
 
6.4%
8 2800
 
6.2%
9 2685
 
6.0%
10 2457
 
5.5%
11 712
 
1.6%
ValueCountFrequency (%)
30 23
0.1%
29 15
< 0.1%
28 29
0.1%
27 23
0.1%
26 20
< 0.1%
25 23
0.1%
24 34
0.1%
23 26
0.1%
22 32
0.1%
21 24
0.1%

loan_intent
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
EDUCATION
9153 
MEDICAL
8548 
VENTURE
7819 
PERSONAL
7552 
DEBTCONSOLIDATION
7145 

Length

Max length17
Median length15
Mean length10.012711
Min length7

Characters and Unicode

Total characters450572
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPERSONAL
2nd rowEDUCATION
3rd rowMEDICAL
4th rowMEDICAL
5th rowMEDICAL

Common Values

ValueCountFrequency (%)
EDUCATION 9153
20.3%
MEDICAL 8548
19.0%
VENTURE 7819
17.4%
PERSONAL 7552
16.8%
DEBTCONSOLIDATION 7145
15.9%
HOMEIMPROVEMENT 4783
10.6%

Length

2024-12-11T21:19:21.893660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-11T21:19:22.357696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
education 9153
20.3%
medical 8548
19.0%
venture 7819
17.4%
personal 7552
16.8%
debtconsolidation 7145
15.9%
homeimprovement 4783
10.6%

Most occurring characters

ValueCountFrequency (%)
E 62385
13.8%
O 47706
10.6%
N 43597
9.7%
I 36774
8.2%
T 36045
8.0%
A 32398
 
7.2%
D 31991
 
7.1%
C 24846
 
5.5%
L 23245
 
5.2%
M 22897
 
5.1%
Other values (7) 88688
19.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 450572
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 62385
13.8%
O 47706
10.6%
N 43597
9.7%
I 36774
8.2%
T 36045
8.0%
A 32398
 
7.2%
D 31991
 
7.1%
C 24846
 
5.5%
L 23245
 
5.2%
M 22897
 
5.1%
Other values (7) 88688
19.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 450572
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 62385
13.8%
O 47706
10.6%
N 43597
9.7%
I 36774
8.2%
T 36045
8.0%
A 32398
 
7.2%
D 31991
 
7.1%
C 24846
 
5.5%
L 23245
 
5.2%
M 22897
 
5.1%
Other values (7) 88688
19.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 450572
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 62385
13.8%
O 47706
10.6%
N 43597
9.7%
I 36774
8.2%
T 36045
8.0%
A 32398
 
7.2%
D 31991
 
7.1%
C 24846
 
5.5%
L 23245
 
5.2%
M 22897
 
5.1%
Other values (7) 88688
19.7%

loan_status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
35000 
1
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 35000
77.8%
1 10000
 
22.2%

Length

2024-12-11T21:19:22.851248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-11T21:19:23.134656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 35000
77.8%
1 10000
 
22.2%

Most occurring characters

ValueCountFrequency (%)
0 35000
77.8%
1 10000
 
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35000
77.8%
1 10000
 
22.2%

Most occurring scripts

ValueCountFrequency (%)
Common 45000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35000
77.8%
1 10000
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35000
77.8%
1 10000
 
22.2%

Interactions

2024-12-11T21:19:11.480636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:18:53.815917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:18:56.451087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:18:58.928631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:01.373127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:04.039201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:06.514745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:08.973841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:11.771119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:18:54.291029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:18:56.775938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:18:59.223820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:01.671693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-11T21:19:09.270044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:12.070929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-11T21:18:57.092918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:18:59.543601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:01.995147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:04.678500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:07.132717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:09.563317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-11T21:18:54.923642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:18:57.402142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:18:59.833403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:02.325258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:04.975466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-11T21:19:03.413281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:05.930794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:08.396433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:10.807867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:13.579805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:18:56.159189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:18:58.623772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:01.056900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:03.708391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:06.216977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:08.686911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T21:19:11.154172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-12-11T21:19:23.370969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
cb_person_cred_hist_lengthcredit_scoreloan_amntloan_int_rateloan_intentloan_percent_incomeloan_statusperson_ageperson_emp_expperson_income
cb_person_cred_hist_length1.0000.1420.0430.0170.054-0.0370.0200.8210.7500.093
credit_score0.1421.0000.0060.0110.016-0.0120.0080.1600.1720.023
loan_amnt0.0430.0061.0000.1050.0300.6660.1260.0640.0520.405
loan_int_rate0.0170.0110.1051.0000.0170.1240.3630.0130.016-0.033
loan_intent0.0540.0160.0300.0171.0000.0180.1420.0300.0290.010
loan_percent_income-0.037-0.0120.6660.1240.0181.0000.415-0.056-0.050-0.353
loan_status0.0200.0080.1260.3630.1420.4151.0000.0120.0140.009
person_age0.8210.1600.0640.0130.030-0.0560.0121.0000.8880.143
person_emp_exp0.7500.1720.0520.0160.029-0.0500.0140.8881.0000.120
person_income0.0930.0230.405-0.0330.010-0.3530.0090.1430.1201.000

Missing values

2024-12-11T21:19:14.005446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-11T21:19:14.553793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

person_ageperson_incomeperson_emp_exploan_amntloan_int_rateloan_percent_incomecredit_scorecb_person_cred_hist_lengthloan_intentloan_status
022.071948.0035000.016.020.495613.0PERSONAL1
121.012282.001000.011.140.085042.0EDUCATION0
225.012438.035500.012.870.446353.0MEDICAL1
323.079753.0035000.015.230.446752.0MEDICAL1
424.066135.0135000.014.270.535864.0MEDICAL1
521.012951.002500.07.140.195322.0VENTURE1
626.093471.0135000.012.420.377013.0EDUCATION1
724.095550.0535000.011.110.375854.0MEDICAL1
824.0100684.0335000.08.900.355442.0PERSONAL1
921.012739.001600.014.740.136403.0VENTURE1
person_ageperson_incomeperson_emp_exploan_amntloan_int_rateloan_percent_incomecredit_scorecb_person_cred_hist_lengthloan_intentloan_status
4499031.0136832.0912319.016.920.097227.0PERSONAL1
4499124.037786.0013500.013.430.366124.0EDUCATION1
4499223.040925.009000.011.010.224874.0PERSONAL1
4499327.035512.045000.015.830.145055.0PERSONAL1
4499424.031924.0212229.010.700.386784.0MEDICAL1
4499527.047971.0615000.015.660.316453.0MEDICAL1
4499637.065800.0179000.014.070.1462111.0HOMEIMPROVEMENT1
4499733.056942.072771.010.020.0566810.0DEBTCONSOLIDATION1
4499829.033164.0412000.013.230.366046.0EDUCATION1
4499924.051609.016665.017.050.136283.0DEBTCONSOLIDATION1